Here we explore an alternative approach, building on the recent framework of Graph Network-based Simulators (GNS) [1]}, [2]}.
Specifically, by not explicitly imposing physical constraints in the model architecture, we can flexibly design expressive GNN models and use the full 3D atomic positions in a scalable manner.
In exchange, the predicted forces are translation-invariant but no longer rotation-covariant.
As we demonstrate empirically, this issue can be alleviated by training models on a massive dataset with rotation data augmentation. In other words, we impose physical constraints to the model implicitly through physics-based data rather than explicitly through architectural constraints. Our model also does not explicitly enforce energy conservation. However, this removes the memory-intensive calculations in physics-based GNNs, i.e., compute forces through energy gradients that require second-order derivatives to optimize.
